Big Data Mining

Deep Learning

Credits: 
1
Hours: 
12
Area: 
Big Data Mining
Academic Year: 
Description: 

The module addresses practical aspects of machine learning and neural networks. It presents and reviews the main technological solutions to solve two machine learning problems: classification and regression. The course covers several crucial aspects to take into account when developing machine/deep learning solutions: i) what is the best solution to adopt for a given problem? ii) how to evaluate a machine learning model? iii) how to optimize it?

Advanced topics in network science

Credits: 
1
Hours: 
12
Area: 
Big Data Mining
Academic Year: 
Description: 

In this course we start from the basic notions of graph theory and self-similar phenomena in order to correctly analyse large socio-economic networks. From this analysis we then proceed in the description of the modelling for various classes of phenomena and to the correct definition of benchmarks through an approach inspired by classical statistical physics.

 

Statistical Methods for Data Science

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Academic Year: 
Description: 

The module presents the basic methodologies, techniques and tools of statistical analysis for data science. This includes basic knowledge of probability theory, random variables, statistical models, estimation theory, hypothesis testing. bootstrap, and basic knowledge of time series analysis. The module shows the applicability in case studies in the domain of financial data science.

Data Science for Quantitive Finance

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Teachers: 
Academic Year: 
Description: 

The course presents the main elements for understanding financial markets, their structure, and technological infrastructure. Specifically, the course provides a background on basic empirical modeling of financial time series, from low to ultrahigh frequency, identifying the key data science aspects including data storage, latency, high dimensional inference, etc. It also covers semantic analysis of texts from news feed and social networks for financial forecasting.

Mobility Data Analysis

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Description: 

The purpose of the course is to introduce the main analysis techniques for spatio-temporal data, with a particular focus on human mobility (including vehicles), aimed to better understand the overall mobility of a territory. The presentation will be supported by several case studies developed with the SoBigData.eu laboratory.

Social Network Analysis

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Description: 

This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at characterizing the structure of large-scale Online Social Networks (OSNs). The course presents both classroom teaching to introduce theoretical concepts, and hands-on computer work to apply the theory on real large-scale datasets obtained from OSNs like Facebook and Twitter.

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